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This repository was archived by the owner on Feb 1, 2024. It is now read-only.
Clarifying Galv's role #118
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backendPython Django/DRF backendPython Django/DRF backendfrontendTypeScript Web frontendTypeScript Web frontend
Description
Outcomes of the meeting to discuss this on July 12th:
🧔 Brady Planden
🧔 Matt Jaquiery
We discussed what Galv should be, and what the benefits of using it will be.
We envision Galv as a "Metadata Secretary", i.e. a (meta)data platform that prompts users to enter high-quality metadata which can then be provided to users at analysis time.
It should serve the needs of both individual researchers and lab managers.
- Frontend updates
- Tiered directory setup that mimics a cycler or standard PC directory
- Decompose monitored paths into subdirectories
- Individual dashboard of tasks (metadata entry) awaiting completion
- New datasets
- Completed/total
- Completed to a particular standard defined by various JSON schemas
- Group dashboard to show Harvester operators (lab manager/PI) how stuff is going
- Final data inspection page that displays the dataset (i.e. how the current inspect element works)
- Move to a more page-by-page view with links between
- Closer functionally to the django-rest-framework frontend but React pretty
- Build monitored paths a directories on landing page with the ability to "subscribe" to read only access & request write access
- Tiered directory setup that mimics a cycler or standard PC directory
- Backend updates
- Monitored Paths for gating access
- Created by Harvester users
- Path is non-editable (destroy/create new if necessary)
- MPs have admin/write/read permissions
- At least one admin chosen at creation time
- Userset modifiable by Harvester admin and MP admin
- Orphan file/dataset views for Harvester users/admins
- Monitored Paths for gating access
- Benefits to users who do their homework
- Lots of work to be done making Harvester parsers work to collect this
- Join together different data sources
- experiment schedule
- equipment details
- cell info
- (see Battery Intelligence Lab examples)
- Scrape several data sources automatically/parse files
- Example scripts that pull this together
- Example datasets with example workflows - Benefits to labs whose members do their homework
- Quick oversight of historical data
- Metaanalysis opportunities
- Ability to track differences in e.g. cell parameters over time
- Benefits to wider community
- Improve collaboration
- Improve data sharing
- Allow large-scale analysis
- Improve adherence to standards e.g. EMMO JSON-LD
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backendPython Django/DRF backendPython Django/DRF backendfrontendTypeScript Web frontendTypeScript Web frontend